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A meta-analysis of use of Prostate Imaging Reporting and Data System Version 2 (PI-RADS V2) with multiparametric MR imaging for the detection of prostate cancer

  • Urogenital
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Abstract

Objectives

This meta-analysis was undertaken to review the diagnostic accuracy of PI-RADS V2 for prostate cancer (PCa) detection with multiparametric MR (mp-MR).

Methods

A comprehensive literature search of electronic databases was performed by two observers independently. Inclusion criteria were original research using the PI-RADS V2 system in reporting prostate MRI. The methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Data necessary to complete 2 × 2 contingency tables were obtained from the included studies.

Results

Thirteen studies (2,049 patients) were analysed. This is an initial meta-analysis of PI-RADs V2 and the overall diagnostic accuracy in diagnosing PCa was as follows: pooled sensitivity, 0.85 (0.78–0.91); pooled specificity, 0.71 (0.60–0.80); pooled positive likelihood ratio (LR+), 2.92 (2.09–4.09); pooled negative likelihood ratio (LR–), 0.21 (0.14–0.31); pooled diagnostic odds ratio (DOR), 14.08 (7.93–25.01), respectively. Positive predictive values ranged from 0.54 to 0.97 and negative predictive values ranged from 0.26 to 0.92.

Conclusion

Currently available evidence indicates that PI-RADS V2 appears to have good diagnostic accuracy in patients with PCa lesions with high sensitivity and moderate specificity. However, no recommendation regarding the best threshold can be provided because of heterogeneity.

Key Points

• PI-RADS V2 shows good diagnostic accuracy for PCa detection.

• Initially pooled specificity of PI-RADS v2 remains moderate.

• PCa detection is increased by experienced radiologists.

• There is currently a high heterogeneity in prostate diagnostics with MRI.

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Abbreviations

95% CI:

95% confidence interval

AUC:

Area under the SROC curve

DCE:

Dynamic contrast-enhanced

DOR:

Diagnostic odds ratio

DWI:

Diffusion-weighted imaging

ESUR:

European Society of Urogenital Radiology

FN:

False negative

FP:

False positive

LR–:

Negative likelihood ratio

LR+:

Positive likelihood ratio

mp-MRI:

Multiparametric MR

MRS:

MR proton spectroscopy

PCa:

Prostate cancer

PI-RADS V2:

Prostate Imaging Reporting and Data System Version 2

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

PSA:

Prostate-specific antigen

PZ:

Peripheral zone

QUADAS-2:

Quality Assessment of Diagnostic Accuracy Studies

SROC:

Summary receiver-operating curve

TN:

True negative

TP:

True positive

TZ:

Transitional zone assessment

References

  1. Ferlay J, Shin HR, Bray F et al (2010) Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 127(12):2893–2917

    Article  CAS  PubMed  Google Scholar 

  2. Barentsz JO, Richenberg J, Clements R et al (2012) ESUR prostate. MR guidelines. Eur Radiol 22(4):746–757

    Article  PubMed  PubMed Central  Google Scholar 

  3. Fütterer JJ, Briganti A, De Visschere P et al (2015) Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? a systematic review of the literature. Eur Urol 68(6):1045–1053

    Article  PubMed  Google Scholar 

  4. Weinreb JC, Barentsz JO, Choyke PL et al (2016) PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2. Eur Urol 69:16–40

    Article  PubMed  Google Scholar 

  5. Lin WC, Muglia VF, Silva GEB et al (2016) Multiparametric MRI of the prostate: diagnostic performance and interreader agreement of two scoring systems. Br J Radiol 89(1062):1–10

    Article  Google Scholar 

  6. Vache T, Bratan F, Mege-lechevallier F et al (2014) Characterization of prostate lesions as benign or malignant at multiparametric MR imaging: comparison of three scoring systems in patients treated with radical prostatectomy. Radiology 272(11):446–455

    Article  PubMed  Google Scholar 

  7. Baris T, Haresh M, Omer A et al (2013) Prostate cancer:can multiparametric mr imaging help identify patients who are candidates for active surveillance? Radiology 268(11):144–152

    Google Scholar 

  8. Rastinehad AR, Waingankar N, Turkbe B et al (2015) Comparison of Multiparametric MRI Scoring Systems and the Impact on Cancer Detection in Patients Undergoing MR US Fusion Guided Prostate Biopsies. PLoS One 10(11):1–16

    Article  Google Scholar 

  9. Hamoen EH, de Rooij M, Witjes JA et al (2014) Use of the prostate imaging reporting and data system (PI-RADS) for prostate cancer detection with multiparametric magnetic resonance imaging: a diagnostic meta-analysis. Eur Urol 67:1112–1121

    Article  PubMed  Google Scholar 

  10. Renard-Penna R, Mozer P, Cornud F et al (2015) Prostate imaging reporting and data system and likert scoring system: multiparametric MR imaging validation study to screen patients for initial biopsy. Radiology 275(2):458–468

    Article  PubMed  Google Scholar 

  11. Roethke MC, Kuru TH, Schultze S et al (2014) Evaluation of the ESUR PI-RADS scoring system for multiparametric MRI of the prostate with targeted MR/TRUS fusion-guided biopsy at 3.0 Tesla. Eur Radiol 24(2):344–352

    Article  CAS  PubMed  Google Scholar 

  12. Schimmoller L, Quentin M, Arsov C et al (2014) Predictive power of the ESUR scoring system for prostate cancer diagnosis verified with targeted MR-guided in-bore biopsy. Eur J Radiol 83(12):2103–2108

    Article  CAS  PubMed  Google Scholar 

  13. Baur ADJ, Maxeiner A, Franiel T et al (2014) Evaluation of the prostate imaging reporting and data system for the detection of prostate cancer by the results of targeted biopsy of the prostate. Investig Radiol 49(6):411–420

    Article  Google Scholar 

  14. Hamoen EH, de Rooij M, Witjes JA et al (2015) Use of the Prostate Imaging Reporting and Data System (PI-RADS) for Prostate Cancer Detection with Multiparametric Magnetic Resonance Imaging: A Diagnostic Meta-analysis. Eur Urol 67(6):1112–1121

    Article  PubMed  Google Scholar 

  15. Moher D, Liberati A, Tetzlaff J et al (2010) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg 8(5):336–341

    Article  PubMed  Google Scholar 

  16. Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529–536

    Article  PubMed  Google Scholar 

  17. Higgins JP, Thompson SG, Deeks JJ et al (2003) Measuring inconsistency in meta-analyses. BMJ 327:557–560

    Article  PubMed  PubMed Central  Google Scholar 

  18. Reitsma JB, Glas AS, Rutjes AW et al (2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58:982–990

    Article  PubMed  Google Scholar 

  19. Zamora J, Abraira V, Muriel A et al (2006) Meta-DiSc: a software for meta-analysis of test accuracy data. BMC Med Res Methodol 6:31

    Article  PubMed  PubMed Central  Google Scholar 

  20. Deeks JJ, Macaskill P, Rwig L (2005) The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol 58:882–893

    Article  PubMed  Google Scholar 

  21. Washino S, Okochi T, Saitoet K et al (2016) Combination of PI-RADS score and PSA density predicts biopsy outcome in biopsy naive patients. BJU Int 2:1–9

    Google Scholar 

  22. Li BS, Wang L, Deng M et al (2016) The correlation between multi-parametric MRI of prostate imaging reporting and data system score and transrectal ultrasound guided needle biopsy. Chin J Magn Reson Imaging 7:5321–5326

    Google Scholar 

  23. Polanec S, Helbich TH, Bickel H et al (2016) Head-to-head comparison of PI-RADS v2 and PI-RADS vl. Eur J Radiol 85(6):1125–1131

    Article  PubMed  Google Scholar 

  24. Park SY, Jung DC, Oh YT et al (2016) Prostate Cancer: PI-RADS Version 2 Helps Preoperatively Predict Clinically Significant Cancers. Radiology 280(1):108–116

    Article  PubMed  Google Scholar 

  25. Mertan FV, Greer MD, Shih JH et al (2016) Prospective Evaluation of the Prostate Imaging Reporting and Data System Version 2 for Prostate Cancer Detection. J Urol 196(3):690–696

    Article  PubMed  Google Scholar 

  26. Meier-Schroers M, Kukuk G, Wolter K et al (2016) Differentiation of prostatitis and prostate cancer using the Prostate Imaging-Reporting and Data System (PI-RADS). Eur J Radiol 85(7):1304–1311

    Article  PubMed  Google Scholar 

  27. Kasel-Seibert M, Lehmann T, Aschenbach R et al (2016) Assessment of PI-RADS V2 for the Detection of Prostate Cancer. Eur J Radiol 85(4):726–731

    Article  PubMed  Google Scholar 

  28. Baldisserotto M, Neto EJ, Carvalhal G et al (2016) Validation of PI-RADS V2 for prostate cancer diagnosis with MRI at 3T using an external phased-array coil. J Magn Reson Imaging 44(5):1354–1359

    Article  PubMed  Google Scholar 

  29. Li P. (2015) The preliminary evaluation of PI-RADS v2 and Likert scale in the diagnosis of prostate cancer using mp-MRI. Suzhou University

  30. Muller BG, Shih JH, Sankineni S et al (2015) Prostate Cancer: Interobserver Agreement and Accuracy with the Revised Prostate Imaging Reporting and Data System at Multiparametric MR Imaging. Radiology 277(3):741–750

    Article  PubMed  PubMed Central  Google Scholar 

  31. Zhao CL, Gao G, Fang D et al (2016) The effi ciency of multiparametric magnetic resonance imaging (mpMRI) using PI-RADS Version 2 in the diagnosis of clinically significant prostate cancer. Clin Imaging 40:885–888

    Article  PubMed  Google Scholar 

  32. Shah ZK, Elias SN, Abaza R et al (2015) Performance comparison of 1.5-T endorectal coil MRI with 3.0-T nonendorectal coil MRI in patients with prostate cancer. Acad Radiol 22(4):467–74.33.

  33. Sertdemir M, Schoenberg SO, Sourbron S, et al. (2013) Interscanner comparison of dynamic contrast-enhanced MRI in prostate cancer: 1.5 versus 3 T MRI. Invest Radiol 48(2):92–97

  34. de Rooij M, Hamoen EH, Futterer JJ et al (2014) Accuracy of multiparametric MRI for prostate cancer detection: a meta-analysis. Am J Roentgenol 202:343–351

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology, Xijing Hospital, Fourth Military Medical University, 17 Changle Road, Xi’an, Shaanxi, 710032, China

    Li Zhang, Sipan Chen & Yi Huan

  2. Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, 710032, China

    Li Zhang, Min Tang, Sipan Chen, Xiaoyan Lei & Xiaoling Zhang

Authors
  1. Li Zhang
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  2. Min Tang
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  3. Sipan Chen
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  4. Xiaoyan Lei
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  5. Xiaoling Zhang
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  6. Yi Huan
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Correspondence to Yi Huan.

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Guarantor

The scientific guarantor of this publication is Guarantor name: Zhang xiaoling zxl.822@163.com

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Funding

The authors state that this work has not received any funding.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was not required for this study because this study is a meta- analysis.

Ethical approval

Institutional Review Board approval was not required because this study is a meta- analysis.

Methodology: diagnostic or prognostic study

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Zhang, L., Tang, M., Chen, S. et al. A meta-analysis of use of Prostate Imaging Reporting and Data System Version 2 (PI-RADS V2) with multiparametric MR imaging for the detection of prostate cancer. Eur Radiol 27, 5204–5214 (2017). https://doi.org/10.1007/s00330-017-4843-7

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  • DOI: https://doi.org/10.1007/s00330-017-4843-7

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